Sensor networks have received considerable attention in recent years, and are employed in many applications. In these applications, statistical aggregates such as Sum over the readings of a group of sensor nodes are often needed. One challenge for computing sensor data aggregates comes from the communication failures, which are common in sensor networks. To enhance the robustness of the aggregate computation, multipath-based aggregation is often used. However, the multipath-based aggregation suffers from the problem of overcounting sensor readings. The approaches using the multipath-based aggregation therefore need to incorporate techniques that avoid overcounting sensor readings. In this paper, we present a novel technique named scalable counting for efficiently avoiding the overcounting problem. We focus on having an (\varepsilon, \delta) accuracy guarantee for computing an aggregate, which ensures that the error in computing the aggregate is within a factor of \varepsilon with probability (1 - \delta). Our schemes using the scalable counting technique efficiently compute the aggregates under a given accuracy guarantee. We provide theoretical analyses that show the advantages of the scalable counting technique over previously proposed techniques. Furthermore, extensive experiments are made to validate the theoretical results and manifest the advantages of using the scalable counting technique for sensor data aggregation.